1. Statement of the Technical Field
The invention concerns computing systems. More particularly, the invention concerns computing systems and methods for efficiently and accurately detecting changes in feature data.
2. Description of the Related Art
Imagery data is analyzed for a variety of reasons, such as for surveillance purposes, quality control purposes and/or change detection purposes. The analysis often involves manually analyzing the imagery data over an area of interest. Such manual analysis is often achieved using a computer executing image analysis software (e.g., ESRI® ArcMap® geospatial information system software, SOCET SET® software, FALCONVIEW® software, ADOBE® PHOTOSHOP® software, computer aided design software, and computer aided manufacturing software). In this scenario, only a portion of a high resolution image is displayed to an operator at any given time. As such, the software provides a pan function and a zoom function. The pan function allows the operator to change a viewport from one part of an image to another part of the image. The zoom function allows the operator to change from a distant view of an image to a more close-up view (zoom in) of the image, and vice versa (zoom out). The pan and zoom operations are typically enabled by the operator using a computer mouse, joy stick and/or gestures.
During the image analysis process, two images of the same scene, taken at different times, can be compared to detect changes in the scene. The changes are detected via a partially automated process or a manual process. In the partially automated process, the contents of a new image are automatically compared to the contents of an old image for purposes of detecting changes in features thereof. Notably, the partially automated process typically results in a relatively large number of false positives. False positives are defined as instances when the partially automated process indicates the presence of a change, but no change actually exists. Therefore, an operator often has to review the results of the automated process to identify and eliminate the false positives. The operator also reviews the results of the automated process to determine whether the change is relevant (e.g., whether a building has been destroyed, replaced or simply expanded). If the change is not relevant, then the automated result is discarded or ignored.
In the manual process, the contents of a new image are manually compared to the contents of an old image for purposes of detecting changes of features therein. More specifically, the manual process involves manually inspecting the area of interest by: (a) obtaining feature data specifying locations and characteristics of a plurality of objects (e.g., gas stations); (b) “panning” to an area of interest within the new image and the old image that is supposed to include a visual representation of at least one of the objects; (c) “zooming in” to obtain a close-up view of the area of interest within the new image and the old image; (d) visually comparing the contents of the new image to the contents of the old image to determine if the object is still present in the area of interest and/or has the characteristics defined by the feature data; and (e) repeating steps (a)-(d) for each of the objects indentified by the feature data. Notably, one or more “panning” and/or “zooming” operations may need to be performed to obtain a desired view of the area of interest. Such a manual inspection is time consuming, costly and subject to human error. Also, such a manual process results in numerous false positives as a result of the change in the sun's angle through out a day, seasonal weather changes and vegetation changes.